{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code run in colab for video embedding and analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 480,
     "status": "ok",
     "timestamp": 1747708154165,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "ka_loh1_sFM2",
    "outputId": "fe57b906-1559-47a3-95f8-bec1cb38bbd4"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1747708154951,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "4zHH71hDvqLx",
    "outputId": "ea6ec0f7-1a10-4a76-88d3-60fdbbe6967b"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "drive_root = '/content/drive/MyDrive'\n",
    "print(\"Contents of MyDrive:\")\n",
    "print(os.listdir(drive_root))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73
    },
    "executionInfo": {
     "elapsed": 4194,
     "status": "ok",
     "timestamp": 1747708164039,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "NifxKUMCwU4H",
    "outputId": "4ed82ef0-7d6d-48df-fc76-56dca557e7c7"
   },
   "outputs": [],
   "source": [
    "from google.colab import files\n",
    "uploaded = files.upload()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 741
    },
    "executionInfo": {
     "elapsed": 36,
     "status": "ok",
     "timestamp": 1747708164987,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "8GT2iwE2xK3x",
    "outputId": "2e2a27a2-99a1-4e90-843e-52ee4c84a5c2"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "labels_df = pd.read_csv('tiktok_with_final_scrape_status.csv')\n",
    "\n",
    "labels_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1747708167827,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "8XcfKRy0xRrN",
    "outputId": "cdf1f65c-726b-4e2b-da77-e266c8881010"
   },
   "outputs": [],
   "source": [
    "labels_df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1747708169259,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "DaLJ8cYaznxM",
    "outputId": "d7c0fb2c-1104-4d23-9478-2961db646fa5"
   },
   "outputs": [],
   "source": [
    "labels_df[\"video_url\"][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1747708170534,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "3u_TQPux0Knx"
   },
   "outputs": [],
   "source": [
    "\n",
    "labels_df['video_id'] = labels_df['video_url'].str.extract(r'video/(\\d+)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 240
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1747708171579,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "pRNFRaFp0Ng5",
    "outputId": "bf45b37c-44ab-4896-f038-971cc1d8b9c5"
   },
   "outputs": [],
   "source": [
    "labels_df['video_id'].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1747708173410,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "OmnzSONl1e1M",
    "outputId": "88eff9b7-a783-48be-9a61-1e2b44017a14"
   },
   "outputs": [],
   "source": [
    "prank_df = labels_df[labels_df['Prank'] == 1].copy()\n",
    "print(f\"{len(prank_df)} videos labeled as prank.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1747708174392,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "cau8LWMO1n7M"
   },
   "outputs": [],
   "source": [
    "prank_df['filename'] = prank_df['video_id'] + '.mp4'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1747708175455,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "zEaAEUVV1u9E",
    "outputId": "8f2d6da0-e91d-4b96-9040-27afae3b3d43"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "tiktok_folder = '/content/drive/MyDrive/TikToks'\n",
    "available_files = set(os.listdir(tiktok_folder))\n",
    "\n",
    "prank_df['file_exists'] = prank_df['filename'].isin(available_files)\n",
    "\n",
    "existing_prank_df = prank_df[prank_df['file_exists']].copy()\n",
    "print(f\"{len(existing_prank_df)} prank videos found in folder.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1747708178694,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "BstRfmc02CDY"
   },
   "outputs": [],
   "source": [
    "existing_prank_df['file_path'] = existing_prank_df['filename'].apply(\n",
    "    lambda x: os.path.join(tiktok_folder, x)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1747708179776,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "fUwvCE6Z30ia",
    "outputId": "bd3c9960-0d8d-49c2-d7d4-9a94a8e3d9c5"
   },
   "outputs": [],
   "source": [
    "existing_prank_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7503,
     "status": "ok",
     "timestamp": 1747708189941,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "_-TRyXTz7bfe",
    "outputId": "bf77624c-0da0-46b0-9a31-e730489eb236"
   },
   "outputs": [],
   "source": [
    "!pip install torch torchvision opencv-python transformers\n",
    "\n",
    "import torch\n",
    "import torchvision.transforms as T\n",
    "import cv2\n",
    "import os\n",
    "from transformers import CLIPProcessor, CLIPModel\n",
    "\n",
    "model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
    "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
    "\n",
    "model.eval() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1747708231069,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "z_7vwCLN7f7x"
   },
   "outputs": [],
   "source": [
    "def extract_frames(video_path, num_frames=8):\n",
    "    cap = cv2.VideoCapture(video_path)\n",
    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "\n",
    "    frame_indices = [int(i * total_frames / num_frames) for i in range(num_frames)]\n",
    "\n",
    "    frames = []\n",
    "    for idx in frame_indices:\n",
    "        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)\n",
    "        ret, frame = cap.read()\n",
    "        if ret:\n",
    "            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "            frames.append(frame)\n",
    "    cap.release()\n",
    "    return frames\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1747708232561,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "W7oZWPrZ7mlX"
   },
   "outputs": [],
   "source": [
    "def get_video_embedding(video_path, num_frames=8):\n",
    "    frames = extract_frames(video_path, num_frames)\n",
    "\n",
    "    inputs = processor(\n",
    "        images=frames,\n",
    "        return_tensors=\"pt\",\n",
    "        padding=True\n",
    "    )\n",
    "\n",
    "    with torch.no_grad():\n",
    "        frame_embeddings = model.get_image_features(**inputs)\n",
    "\n",
    "    return frame_embeddings.mean(dim=0)  # average across frames\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 141813,
     "status": "ok",
     "timestamp": 1747698529850,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "imq23uVt7pyx",
    "outputId": "b094cf34-d19e-418b-c6f3-892b45afccd9"
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "prank_embeddings = {}\n",
    "\n",
    "for video_path in tqdm(existing_prank_df['file_path']):\n",
    "    video_id = os.path.basename(video_path).replace('.mp4', '')\n",
    "    try:\n",
    "        emb = get_video_embedding(video_path)\n",
    "        prank_embeddings[video_id] = emb\n",
    "    except Exception as e:\n",
    "        print(f\"Error with {video_id}: {e}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1747698590609,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "VfKBtdtN7tBC"
   },
   "outputs": [],
   "source": [
    "\n",
    "prank_tensor = torch.stack(list(prank_embeddings.values()))\n",
    "prank_mean_embedding = prank_tensor.mean(dim=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 104,
     "status": "ok",
     "timestamp": 1747708269857,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "QW-toZZS8Rmu"
   },
   "outputs": [],
   "source": [
    "!mkdir -p /content/drive/MyDrive/genre_embeddings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "executionInfo": {
     "elapsed": 169,
     "status": "error",
     "timestamp": 1747708006345,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "LX3_0ELH9QqX",
    "outputId": "d535c9b4-df1c-41fb-dfeb-8110e5ef5ca0"
   },
   "outputs": [],
   "source": [
    "np.save('/content/drive/MyDrive/genre_embeddings/prank.npy', prank_np)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1747708286419,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "ixO_l0ZA9eLe"
   },
   "outputs": [],
   "source": [
    "\n",
    "genre_cols = [\n",
    "    'Prank',\n",
    "    'HPV (hypnotic process video)',\n",
    "    'Media Commentary/Reaction',\n",
    "    'Human Experiment',\n",
    "    'CPB Common Person Depiction/Experience',\n",
    "    'Music/Dance/Acting',\n",
    "    'Vlog',\n",
    "    'Skit',\n",
    "    'Stunts/Sports',\n",
    "    'Facial Animation/Cosplay',\n",
    "    'Advertisement (Overt)'\n",
    "]\n",
    "\n",
    "tiktok_folder = '/content/drive/MyDrive/TikToks'\n",
    "\n",
    "available_files = set(os.listdir(tiktok_folder))\n",
    "\n",
    "genre_dfs = {}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 30,
     "status": "ok",
     "timestamp": 1747708288527,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "KIHI1tTb9iAh",
    "outputId": "52bafbbf-a5cd-458e-ae17-7d71eccef17f"
   },
   "outputs": [],
   "source": [
    "for genre in genre_cols:\n",
    "\n",
    "    genre_df = labels_df[labels_df[genre] == 1].copy()\n",
    "\n",
    "    genre_df['filename'] = genre_df['video_id'] + '.mp4'\n",
    "    genre_df['file_exists'] = genre_df['filename'].isin(available_files)\n",
    "\n",
    "    existing_df = genre_df[genre_df['file_exists']].copy()\n",
    "\n",
    "    existing_df['file_path'] = existing_df['filename'].apply(\n",
    "        lambda x: os.path.join(tiktok_folder, x)\n",
    "    )\n",
    "\n",
    "    genre_dfs[genre] = existing_df\n",
    "\n",
    "    print(f\"{genre}: {len(existing_df)} videos available\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1747708290743,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "uJEnLc3W90We"
   },
   "outputs": [],
   "source": [
    "def embed_and_save_genre(genre_name, genre_df, save_dir='/content/drive/MyDrive/genre_embeddings'):\n",
    "    import torch\n",
    "    import numpy as np\n",
    "    from tqdm import tqdm\n",
    "    import os\n",
    "    import re\n",
    "\n",
    "    embeddings = {}\n",
    "\n",
    "    for path in tqdm(genre_df['file_path'], desc=f'Processing {genre_name}'):\n",
    "        video_id = os.path.basename(path).replace('.mp4', '')\n",
    "        try:\n",
    "            emb = get_video_embedding(path)\n",
    "            embeddings[video_id] = emb\n",
    "        except Exception as e:\n",
    "            print(f\"⚠️ Error with {video_id}: {e}\")\n",
    "\n",
    "    if not embeddings:\n",
    "        print(f\"No embeddings generated for {genre_name}\")\n",
    "        return\n",
    "\n",
    "    tensor = torch.stack(list(embeddings.values()))\n",
    "    mean_embedding = tensor.mean(dim=0).cpu().numpy()\n",
    "\n",
    "    safe_genre_name = re.sub(r'[\\\\/:\"*?<>|]+', '_', genre_name)\n",
    "\n",
    "    os.makedirs(save_dir, exist_ok=True)\n",
    "    save_path = os.path.join(save_dir, f\"{safe_genre_name}.npy\")\n",
    "    np.save(save_path, mean_embedding)\n",
    "\n",
    "    print(f\"✅ Saved {genre_name} embedding to: {save_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1508217,
     "status": "ok",
     "timestamp": 1747709801622,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "vNXYf65k93u3",
    "outputId": "f1078ce7-edfa-4832-d68e-ad60445337d7"
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "for genre in genre_cols:\n",
    "    if genre == 'Prank':\n",
    "      continue\n",
    "    safe_genre_name = re.sub(r'[\\\\/:\"*?<>|]+', '_', genre)\n",
    "    save_path = f'/content/drive/MyDrive/genre_embeddings/{safe_genre_name}.npy'\n",
    "\n",
    "    if os.path.exists(save_path):\n",
    "        print(f\"✅ Already exists: {safe_genre_name}, skipping.\")\n",
    "        continue\n",
    "\n",
    "    genre_df = genre_dfs[genre]\n",
    "\n",
    "    if genre_df.empty:\n",
    "        print(f\"⚠️ No videos found for genre: {genre}\")\n",
    "        continue\n",
    "\n",
    "    embed_and_save_genre(genre, genre_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1148,
     "status": "ok",
     "timestamp": 1747710001455,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "Vyjkbr2TkxKo",
    "outputId": "2f9fc2d6-8ef0-4ef5-d500-f92e4f3e4806"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "embedding_dir = '/content/drive/MyDrive/genre_embeddings'\n",
    "\n",
    "prank_vector = np.load(os.path.join(embedding_dir, 'prank.npy'))\n",
    "\n",
    "all_files = os.listdir(embedding_dir)\n",
    "\n",
    "distances = []\n",
    "\n",
    "for filename in all_files:\n",
    "    if not filename.endswith('.npy'):\n",
    "        continue\n",
    "\n",
    "    genre_name = filename.replace('.npy', '')\n",
    "    if genre_name == 'Prank':\n",
    "        continue\n",
    "\n",
    "    vec = np.load(os.path.join(embedding_dir, filename))\n",
    "\n",
    "    dist = cosine_distances([prank_vector], [vec])[0][0]\n",
    "    distances.append((genre_name, dist))\n",
    "\n",
    "distances.sort(key=lambda x: x[1])\n",
    "\n",
    "for genre, d in distances:\n",
    "    print(f\"{genre}: {d:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 607
    },
    "executionInfo": {
     "elapsed": 231,
     "status": "ok",
     "timestamp": 1747710763260,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "6xNQocfCraCb",
    "outputId": "c6409394-9d0a-46bc-c182-3af2d3c1807e"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "labels = [\n",
    "    \"Skit\",\n",
    "    \"Vlog\",\n",
    "    \"Human Experiment\",\n",
    "    \"Music/Dance/\\nActing\",\n",
    "    \"Media Commentary/\\nReaction\",\n",
    "    \"Advertisement\\n(Overt)\",\n",
    "    \"Common Person/\\nExperience Depiction\",\n",
    "    \"Facial Animation/\\nCosplay\",\n",
    "    \"Mesmeric Process\\nVideo\",\n",
    "    \"Stunts/Sports\"\n",
    "]\n",
    "\n",
    "distances = [\n",
    "    0.0195,\n",
    "    0.0207,\n",
    "    0.0214,\n",
    "    0.0314,\n",
    "    0.0335,\n",
    "    0.0369,\n",
    "    0.0385,\n",
    "    0.0385,\n",
    "    0.0424,\n",
    "    0.1501\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(labels, distances, color='skyblue')\n",
    "plt.xlabel(\"Cosine Distance\")\n",
    "plt.title(\"TikTok Genres by Cosine Distance From Pranks, via CLIP Embeddings\")\n",
    "plt.gca().invert_yaxis()\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xkWF4IiC8-0G"
   },
   "source": [
    "YouTube; run independently from above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20755,
     "status": "ok",
     "timestamp": 1747719359976,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "RA1geJki8-P4",
    "outputId": "fe961802-18cf-4b96-d84f-5a4265d88d3b"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1747719359994,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "cOHMbB5oD75U"
   },
   "outputs": [],
   "source": [
    "youtube_folder = '/content/drive/MyDrive/YouTube'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1908,
     "status": "ok",
     "timestamp": 1747719363935,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "-0ziGBQwEJb8",
    "outputId": "6ffb71ee-d8a4-4f54-e256-4e925125a99f"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "youtube_files = os.listdir(youtube_folder)\n",
    "print(f\"Found {len(youtube_files)} YouTube videos.\")\n",
    "print(\"Sample files:\", youtube_files[:5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73
    },
    "executionInfo": {
     "elapsed": 5166,
     "status": "ok",
     "timestamp": 1747719369865,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "br65UBaWGMpd",
    "outputId": "276c5263-20fe-44ba-82f6-6ba429c9a594"
   },
   "outputs": [],
   "source": [
    "from google.colab import files\n",
    "uploaded = files.upload()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 899,
     "status": "ok",
     "timestamp": 1747719371466,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "-LBSDXOLH3af"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "youtube_df = pd.read_csv('youtube_with_final_scrape_status.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1747719372853,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "otUspe34Hy9p",
    "outputId": "fb79dc3f-8e23-41ef-a9ce-0ed32aedc4d6"
   },
   "outputs": [],
   "source": [
    "youtube_df.columns.tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1747719375283,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "Gzj6QsKMIA62"
   },
   "outputs": [],
   "source": [
    "\n",
    "column_renames = {\n",
    "    'Prank ': 'Prank',\n",
    "    'HPV (Hypnotic Process Video_': 'HPV',\n",
    "    'Media Commentary or Reaction': 'Media Commentary',\n",
    "    'Common Person/Experience Depiction': 'Common Person Experience',\n",
    "    'Video Essay': 'Video Essay',\n",
    "    'Human Experiment': 'Human Experiment',\n",
    "    'Gaming': 'Gaming',\n",
    "    'Vlog': 'Vlog',\n",
    "    'How To': 'How To',\n",
    "    'Skit': 'Skit',\n",
    "    'Stunts/Sports': 'Stunts Sports',\n",
    "    'Ads': 'Advertisement'\n",
    "}\n",
    "\n",
    "\n",
    "youtube_df.rename(columns=column_renames, inplace=True)\n",
    "\n",
    "\n",
    "youtube_genres_clean = list(column_renames.values())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 52,
     "status": "ok",
     "timestamp": 1747719731650,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "xSMYEyzWNqQ_",
    "outputId": "5e141e06-92cd-4427-a2df-4ab756f413da"
   },
   "outputs": [],
   "source": [
    "\n",
    "youtube_folder = '/content/drive/MyDrive/YouTube'\n",
    "available_youtube_files = set(os.listdir(youtube_folder))\n",
    "\n",
    "youtube_df['filename'] = youtube_df['videoId'].astype(str) + '.mp4'\n",
    "youtube_df['file_exists'] = youtube_df['filename'].isin(available_youtube_files)\n",
    "\n",
    "genre_dfs_youtube = {}\n",
    "\n",
    "for genre in youtube_genres_clean:\n",
    "    df = youtube_df[(youtube_df[genre] == 1) & (youtube_df['file_exists'])].copy()\n",
    "    df['file_path'] = df['filename'].apply(lambda x: os.path.join(youtube_folder, x))\n",
    "    genre_dfs_youtube[genre] = df\n",
    "    print(f\"{genre}: {len(df)} videos available\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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      "eb7f8796e5e94e6a91f80d5bbfb5b40b",
      "9085bd319c79441aa9be23977c50ec3d",
      "853ddbf6979249e9a6253ecca3e7ec7e"
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    },
    "executionInfo": {
     "elapsed": 117555,
     "status": "ok",
     "timestamp": 1747719494883,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "EkiZgHNsJ6TS",
    "outputId": "6b872cd3-3ff9-44e5-a105-59fda1804807"
   },
   "outputs": [],
   "source": [
    "!pip install torch torchvision opencv-python transformers\n",
    "\n",
    "import torch\n",
    "import torchvision.transforms as T\n",
    "import cv2\n",
    "import os\n",
    "from transformers import CLIPProcessor, CLIPModel\n",
    "\n",
    "model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
    "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
    "model.eval()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1747719494889,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "9vwcQsS0KD8h"
   },
   "outputs": [],
   "source": [
    "def extract_frames(video_path, num_frames=8):\n",
    "    cap = cv2.VideoCapture(video_path)\n",
    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "\n",
    "    if total_frames == 0:\n",
    "        cap.release()\n",
    "        raise ValueError(f\"No frames found in video: {video_path}\")\n",
    "\n",
    "    frame_indices = [int(i * total_frames / num_frames) for i in range(num_frames)]\n",
    "\n",
    "    frames = []\n",
    "    for idx in frame_indices:\n",
    "        if idx >= total_frames:\n",
    "            continue\n",
    "        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)\n",
    "        ret, frame = cap.read()\n",
    "        if ret:\n",
    "            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "            frames.append(frame)\n",
    "    cap.release()\n",
    "\n",
    "    if not frames:\n",
    "        raise ValueError(f\"Could not read any frames from: {video_path}\")\n",
    "\n",
    "    return frames\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1747719528262,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "Zt-fjId8KItt"
   },
   "outputs": [],
   "source": [
    "def get_video_embedding(video_path, num_frames=8):\n",
    "    frames = extract_frames(video_path, num_frames)\n",
    "\n",
    "    inputs = processor(\n",
    "        images=frames,\n",
    "        return_tensors=\"pt\",\n",
    "        padding=True\n",
    "    )\n",
    "\n",
    "    with torch.no_grad():\n",
    "        frame_embeddings = model.get_image_features(**inputs)\n",
    "\n",
    "    return frame_embeddings.mean(dim=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1747719770788,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "44KD8qTXKLQO"
   },
   "outputs": [],
   "source": [
    "def embed_and_save_genre(genre_name, genre_df, save_dir='/content/drive/MyDrive/genre_embeddings_yt'):\n",
    "    import torch\n",
    "    import numpy as np\n",
    "    from tqdm import tqdm\n",
    "    import os\n",
    "    import re\n",
    "\n",
    "    embeddings = {}\n",
    "\n",
    "    for path in tqdm(genre_df['file_path'], desc=f\"Processing {genre_name}\"):\n",
    "        video_id = os.path.basename(path).replace('.mp4', '')\n",
    "        try:\n",
    "            emb = get_video_embedding(path)\n",
    "            embeddings[video_id] = emb\n",
    "        except Exception as e:\n",
    "            print(f\"⚠️ Error with {video_id}: {e}\")\n",
    "\n",
    "    if not embeddings:\n",
    "        print(f\"No embeddings generated for {genre_name}\")\n",
    "        return\n",
    "\n",
    "    tensor = torch.stack(list(embeddings.values()))\n",
    "    mean_embedding = tensor.mean(dim=0).cpu().numpy()\n",
    "\n",
    "    safe_genre_name = re.sub(r'[\\\\/:\"*?<>|]+', '_', genre_name)\n",
    "    os.makedirs(save_dir, exist_ok=True)\n",
    "    save_path = os.path.join(save_dir, f\"{safe_genre_name}.npy\")\n",
    "\n",
    "    np.save(save_path, mean_embedding)\n",
    "    print(f\"✅ Saved {genre_name} embedding to: {save_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1069050,
     "status": "ok",
     "timestamp": 1747720892178,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
     "user_tz": 300
    },
    "id": "Pw1l_DejKOPW",
    "outputId": "3928e84c-b380-4be9-a9c3-6ac0cbf97ab8"
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "for genre, df in genre_dfs_youtube.items():\n",
    "    safe_name = re.sub(r'[\\\\/:\"*?<>|]+', '_', genre)\n",
    "    save_path = f'/content/drive/MyDrive/genre_embeddings_yt/{safe_name}.npy'\n",
    "\n",
    "    if os.path.exists(save_path):\n",
    "        print(f\"✅ Already exists: {genre}, skipping.\")\n",
    "        continue\n",
    "\n",
    "    if df.empty:\n",
    "        print(f\"⚠️ No videos for {genre}\")\n",
    "        continue\n",
    "\n",
    "    embed_and_save_genre(genre, df)\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1524,
     "status": "ok",
     "timestamp": 1747721137287,
     "user": {
      "displayName": "Tess McNulty",
      "userId": "06651063994343005574"
     },
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    },
    "id": "QF1-92c6Ovjo",
    "outputId": "c78c6573-f6d0-424c-8988-5cdd09afa411"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics.pairwise import cosine_distances\n",
    "\n",
    "yt_embedding_dir = '/content/drive/MyDrive/genre_embeddings_yt'\n",
    "\n",
    "prank_vec = np.load(os.path.join(yt_embedding_dir, 'Prank.npy'))\n",
    "\n",
    "all_files = os.listdir(yt_embedding_dir)\n",
    "\n",
    "yt_distances = []\n",
    "\n",
    "for file in all_files:\n",
    "    if not file.endswith('.npy'):\n",
    "        continue\n",
    "\n",
    "    genre = file.replace('.npy', '')\n",
    "\n",
    "    if genre == 'Prank':\n",
    "        continue  \n",
    "\n",
    "    vec = np.load(os.path.join(yt_embedding_dir, file))\n",
    "    dist = cosine_distances([prank_vec], [vec])[0][0]\n",
    "    yt_distances.append((genre, dist))\n",
    "\n",
    "yt_distances.sort(key=lambda x: x[1])\n",
    "\n",
    "print(\"YouTube Genre Distances from Prank:\\n\")\n",
    "for genre, d in yt_distances:\n",
    "    print(f\"{genre}: {d:.4f}\")\n"
   ]
  },
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    "colab": {
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    "executionInfo": {
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   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "yt_labels = [\n",
    "    \"Human Experiment\",\n",
    "    \"Vlog\",\n",
    "    \"Video Essay\",\n",
    "    \"Advertisement\\n(Overt)\",\n",
    "    \"Skit\",\n",
    "    \"Media Commentary/\\nReaction\",\n",
    "    \"Common Person/\\nExperience\",\n",
    "    \"How To\",\n",
    "    \"MPV\",\n",
    "    \"Stunts/Sports\",\n",
    "    \"Gaming\"\n",
    "]\n",
    "\n",
    "yt_distances = [\n",
    "    0.0561,\n",
    "    0.0571,\n",
    "    0.0629,\n",
    "    0.0647,\n",
    "    0.0648,\n",
    "    0.0697,\n",
    "    0.0721,\n",
    "    0.0934,\n",
    "    0.1023,\n",
    "    0.1124,\n",
    "    0.1835\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(yt_labels, yt_distances, color='lightgreen')\n",
    "plt.xlabel(\"Cosine Distance\")\n",
    "plt.title(\"YouTube Genres by Cosine Distance From Pranks, via CLIP Embeddings\")\n",
    "plt.gca().invert_yaxis()\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
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